In the information age, storage and management of electronic documents (digital content) is an increasingly challenging problem.
Classification of electronic documents was originally a manual task—the function of classifying an electronic document to determine whether and for how long to archive the document was performed by the document's author or by some other records manager or archivist.
For records managers and others responsible for building and enforcing document classification policies, retention schedules, and other aspects of a records management program, the problem with traditional manual classification methods is that content needs to be understood to determine why and for how long it must be retained. Managing the retention and destruction of information reduces litigation risk, reduces e-discovery and digital archiving costs, and ensures compliance with any regulatory standards.
Many users view the process of sorting records from transient content as time-consuming and sometimes even exasperating. In addition, the ubiquity of mobile devices and social media applications makes it difficult to build standard classification tools into end-user applications.
Furthermore, records managers also struggle with enforcing policies that rely on manual, human-based approaches. Accuracy and consistency in applying classification is often inadequate when left to users, the costs in terms of productivity loss are high, and these issues, in turn, result in increased business and legal risk as well as the potential for the entire records management program to quickly become unsustainable in terms of its ability to scale.
It will be noted that throughout the appended drawings, like features are identified by like reference numerals.